Many diagnostic systems are currently available for detecting and diagnosing faults. These systems are particularly important for machines that are used in unfriendly environments, such as down-well or sub-sea equipment of the type used in the oil industry, and where minimising machine downtime is critical. In both these cases, fast and effective methods for diagnosing faults are desirable.
EP 1 136 912 A2 describes a diagnostic engine that uses a model-based technique. Model-based diagnosis involves using a mathematical model of the target system. Different components are represented by the variables of the model. To diagnose a fault the variables are changed until the modelled behaviour of the system matches the observed behaviour. Variables that are different to their normal values are nominated as faulty. In EP 1 136 912 A2, the model-based paradigm incorporates probability theory in the form of Bayes theorem. The model contains information on the coverage of tests on particular components, and probabilistic dependencies between the tests. The diagnosis engine then sets different components as faulty and determines the probability that they are actually at fault. In this way, the most likely source of the fault can be determined.
Another system that uses model based diagnosis is described in EP 0 871 126 A2. In this, when a fault is detected multiple models or hypotheses are generated, with each one containing a different set of faulty components. This is called constraint suppression. Observed machine signals are then propagated through the models/hypotheses using a qualitative physics model. This type of model specifies the mathematical relationship between variables/components in a way that reduces computational intensity. By matching a hypothesis with the observed behaviour, faults can be detected. U.S. Pat. No. 5,132,920 describes another diagnosis system, which combines model based diagnosis and rule based diagnosis. Rule based diagnosis generally involves storing if-then rules, for instance: ‘IF this_sensor_reading THEN that_component_is_faulty’. This requires all faults and their related sensor readings to be predicted in advance. Model-based diagnosis involves using a mathematical model of the system, where different components are represented by the variables of the model. A rule base is the simplest and quickest form of diagnosis, whilst model based diagnosis takes a relatively long period of time. In U.S. Pat. No. 5,132,920, the rule base is used to home in on the likely cause of the fault in order to save time before handing over to model based diagnostics.
U.S. Pat. No. 5,150,367 describes a method of enhancing model based diagnosis that uses constraint propagation control. Conventionally, if a fault has been detected at some test point then the signal at that point will differ from the normal signal. The effects of the new signal can be propagated through the model to determine its causes or effects on other signals. In this way, the component producing the faulty signal, i.e. causing the fault, can be determined. U.S. Pat. No. 5,150,367 discloses a mathematical way of coping with multiple propagation, where more than one fault signal has been propagated through the model to a particular component.
U.S. Pat. No. 5,633,800 describes yet another diagnostic system, in this case specifically adapted for rotating machinery. This involves measuring the actual response in the machinery that is to be diagnosed, and determining a probable cause of the mechanical problem based on the actual response. Once this is done, a model of the machinery is selected based on the probable cause, and a predicted response is determined. Then, the model is modified so that the predicted response and the actual response are substantially in agreement. In this way, the mechanical problem can be identified.
Another known approach to diagnostics is to use integrated diagnostic systems. These use a plurality of different diagnostics tools to provide an integrated diagnostic outcome. These systems can improve the diagnostic performance over that of individual diagnostic tools. However, to date there have been only a few attempts at presenting a unified framework for integrated diagnostics, and most concentrate on military weapons programmes. In these systems, in order to fuse differing diagnostic tools a constraint is placed on the tool providers, ensuring that each tool provides a confidence level for each individual fault. Such systems do not generally detail their method of integrating diagnostic tools that operate at different sampling frequencies.
To overcome some of the problems with existing systems, the U.S. Army is developing an integrated diagnostics system, which it refers to as a ‘Prognostic Framework’. This is described by L. P. Su, M. Nolan, G. de Mare, and D. Carey in the article “Prognostics framework [for weapon systems health monitoring]”, published in AUTOTESTCON Proceedings, IEEE Systems Readiness Technology Conference, 1999, pages 661-672. IEEE, 1999. This is aimed at integrating logistical infrastructure with embedded diagnostics. The foundations of this framework are hierarchical modelling and the separation of test and diagnostic functions. The core of the Prognostic Framework is a design based model, called the fault propagation model that consists of relationships between faults and symptoms. This model is essentially a two dimensional matrix that maps information from raw sensor data, embedded diagnostic tools, pilot debriefing, etc to known faults. A set of intelligent algorithms, collectively known as the diagnostician, then operates on this matrix to isolate faults from given symptoms. The model maps sensor data to physical components. Using this model built-in test information can be extended to diagnosis. A problem with this technique is, however, that to build the matrix all faults and their symptoms have to be foreseen in advance.
Another approach to diagnosis is that used in the relatively new field of diagnostic fusion. This aims to overcome the limitations of using a single diagnostic tool by fusing together the responses from different types of tool. In this way the weaknesses of a rule base may be augmented by the strengths of, say, a neural network to provide a powerful diagnostic system.
Fused diagnostic systems have been around for some time in the form of hybrids. These use diagnostic information fusion to determine a system's state for those instances where several different diagnostic tools, and possible other sources, are used for state estimation. Details of such systems are described in the article “Fusing diagnostic information without a priori performance knowledge” by M. Garbiras and K. Goebel, in the Proceedings of the Third International Conference on Information Fusion, 2000, volume 6, pages 9-16. IEEE, 2000. There are, however, various problems associated with the fusion method proposed by Garbiras et al. For example when information is expressed in different design domains, such as probabilistic information, binary information or weights, the fusion scheme needs to map the different domains into a common one to be able to properly use the encoded data. In addition, the fusion scheme has to deal with diagnostic tools that operate at different sampling frequencies. Furthermore, if diagnostic tools disagree, a decision has to be taken as to which tool to believe and to what degree. Full details of these issues are described in the article “Diagnostic information fusion: requirements flow down and interface issues” by K. Goebel, M. Krok, and H. Sutherland, which is published in the Aerospace Conference Proceedings, 2000, volume 6, pages 155-162. IEEE, 2000.
An alternative, more limited approach is proposed by M. Garbiras and K. Goebel in the article “Fusing diagnostic information without a priori performance knowledge”, published in the Proceedings of the Third International Conference on Information Fusion, 2000, volume 6, pages 9-16. IEEE, 2000. In this approach, a neural network is used to fuse the outputs of different diagnostic tools, and then focus on providing a system to recognise faults without a priori knowledge of the system. A disadvantage of this approach is that useful design information is ignored.
Despite much work in this field, integrated or fused diagnostic systems are relatively limited. This is confirmed by a study recently conducted by the United States Department of Defence. The results of this study were published by S. Freschi et al, see the article “Open systems integrated diagnostics demonstration (OSAIDD) study”, Technical report, Office of the Secretary of Defense, USA, 250 January 1999. One of the key findings of the OSAIDD study was that a consistent approach to integrating diagnostic functions does not exist. The study recommended the use of an information-based, open systems approach to defining and integrating diagnostic functions within the components of a generic architecture of hardware and software elements. This is described in more detail by S. Freschi in the article “Cost and benefit considerations for implementing an open systems approach to integrated diagnostics”, published in the proceedings of AUTOTESTCON '99; The IEEE Systems Readiness Technology Conference, pages 391-404. IEEE, Aug. 30 to Sep. 2, 1999.
The architecture proposed by Freschi et al is shown in FIG. 1. The basic premise of the recommended approach is the concept of a formal model of diagnostic information, which is shared by all participants in the system test and diagnosis process. The mechanism for this approach is an information model, which is a rigourous, formal specification of the information used within the system test and diagnostic domain. Optimising this process comes from sharing this information throughout the diagnostic process. The mechanism for the recommended Information Model is shown in FIG. 2. The interface between diagnostic tools is based on the internationally and commonly used Open Systems Interconnection 7 layer network model, as shown in FIG. 3. This proposed interface concentrates on the passing of information via different physical and software technologies and, as such, is a good choice for integrated diagnostics. However, there is no detail on how to actually interface the different types of information produced by different diagnostic paradigms.
The prior art systems described above all suffer from the same problem. They are all individual diagnostic tools, designed only to take in specific forms of sensor data and provide a diagnosis. Although some of them are integrated diagnostic systems or fusion systems, they are designed to fuse only particular systems, they are not designed to be a generic framework for fault detection and diagnosis, nor are they able to fuse a multitude of different types of tools and information. To overcome some of these problems, a system using an integrated heterogeneous knowledge approach has been proposed. This is described by Hamilton et al in “Fault Diagnosis on Autonomous Robotic Vehicles with RECOVERY”, Proceedings of the 2001 IEEE International Conference on Robotics & Automation, Seoul, Korea—May 21-26, 2001. The present invention builds on the system described in this article.